Information-theoretic feature selection for functional data classification

Gómez-Verdejo, Vanessa; Verleysen, Michel; Fleury, Jérôme

doi:10.1016/j.neucom.2008.12.035

Cited by 53 publications

(26 citation statements)

References 10 publications

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“…And the selection procedure will be terminated if the number of selected features is larger then the user-specified threshold d. In this work, we mainly focused on the method for ranking the features. To determine the value of d, some alternative technologies, such as the inconsistent rate [39], permutation test [40] or wrappers [7], can be adopted. For each candidate feature f 2 F do (6) Calculate its value of criterion J(f) = R(f, class) Â w(f);…”

Section: Selection Algorithmmentioning

confidence: 99%

Feature selection using dynamic weights for classification

Sun

Liu

et al. 2013

Knowledge-Based Systems

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Section: Selection Algorithmmentioning

confidence: 99%

Feature selection using dynamic weights for classification

Sun

Liu

et al. 2013

Knowledge-Based Systems

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“…A forward-backward sequential supervised feature selection algorithm 6,11 is implemented in the experiments as follows: during the forward step the selected feature subset S starts empty; at each iteration the feature f i that together with S has the largest MI with Y , is permanently added to S. The procedure continues until a given stopping criterion is reached. The backward step starts from the final subset S of the forward step.…”

Section: Methodsmentioning

confidence: 99%

A Mutual Information estimator for continuous and discrete variables applied to Feature Selection and Classification problems

Coelho¹,

Braga²,

Verleysen³

2016

IJCIS

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Currently Mutual Information has been widely used in pattern recognition and feature selection problems. It may be used as a measure of redundancy between features as well as a measure of dependency evaluating the relevance of each feature. Since marginal densities of real datasets are not usually known in advance, mutual information should be evaluated by estimation. There are mutual information estimators in the literature that were specifically designed for continuous or for discrete variables, however, most real problems are composed by a mixture of both. There is, of course, some implicit loss of information when using one of them to deal with mixed continuous and discrete variables. This paper presents a new estimator that is able to deal with mixed set of variables. It is shown in experiments with synthetic and real datasets that the method yields reliable results in such circumstance.

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“…Although each method has its advantages and disadvantages, mutual information has proven to be an appropriate measure in several applications such as selection of spectral variables, spectrometric nonlinear modelling and functional data classification, see Gomez-Verdejo et al (2009);Rossi et al (2007;. Moreover, as discussed in Cover & Thomas (1991), correlation does not measure nonlinear relations among features and wrapper approach presents a high computational…”

Section: Subset Relevant Assessmentmentioning

confidence: 99%